Human Activity Recognition via Time Series Imaging of Wearable Sensors and Fusion of Multiple Deep CNN Models

Human Activity Recognition (HAR) plays a vital role in healthcare, smart homes, and security applications. Recent advancements in sensor technology and machine learning have significantly improved HAR’s capability to address real-world challenges, particularly in continuous monitoring of elderly individuals. This study proposes a HAR system utilizing the publicly available GOTOV dataset, which contains tri-axial accelerometer data collected from ankle, chest, and wrist sensors. The dataset comprises 16 activity classes, including several kinematically similar activities that present classification challenges. Current state-of-the-art (SOTA) methods demonstrate limited performance in distinguishing these similar activities. To overcome this limitation, we developed a novel Precision-Weighted Models Fusion (PWMF) algorithm that optimally combines predictions from multiple sensor-specific models. The time-series signals were first transformed into images using Gramian Angular Field (GAF) representation to facilitate training of Convolutional Neural Network (CNN) models for each sensor modality. Our fusion-based approach achieved an F1-score of 93% across all activity classes, demonstrating superior performance in multi-class human activity recognition compared to existing methods.

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